The present disclosure generally relates to pest monitoring and control for crops and more particularly relates to a system and method for automatically monitoring pests using cognitive image recognition with different types of cameras on autonomous vehicles.
Pest control is an important task for agriculture to increase crop production. Monitoring plays a key role in pest control systems. Monitoring allows crop producers to identify the distribution of pests over their land and evaluate the impact of these pests on crop yield and quality. In addition, monitoring provides an ongoing pest history of the farms to improve farm management. Conventional monitoring mainly relies on traps (e.g., sticky trap, wing trap, bucket trap, pan trap, pitfall trap, light trap, etc.) or farmers sometimes capture pests themselves using vacuums or nets.
More recent methods of pest monitoring involve using drones to capture images of the plants and identify pests in the images. However, due to a variety of obstacles, such as the size and color of the plants and pests in relation to one another or the location of the pests on the plants (e.g., underside of leaves), pest identification and monitoring remains a constant challenge.